Cross-domain action prediction

ABSTRACT

One or more computing devices, systems, and/or methods for cross-domain action prediction are provided. Action sequence embeddings are generated based upon a textual embedding and a graph embedding utilizing past user action sequences corresponding to sequences of past actions performed by users across a plurality of domains. An autoencoder is trained to utilize the action sequence embeddings to project the action sequence embeddings to obtain intent space vectors. A service switch classifier is trained using the intent space vectors. In response to the service switch classifier predicting that a current user will switch from a current domain to a next domain, the current user is provided with a recommendation of an action corresponding to the next domain.

BACKGROUND

Many users may perform tasks across a variety of services, applications,platforms, and/or websites corresponding to different types of domains,such as a news application having a news domain, a shopping websitehaving a shopping domain, a social network service having a socialnetworking domain, etc. For example, a user may utilize a user device toaccess the news application having the news domain. The user may readvarious articles pertaining to topics that may be interesting to theuser, such as an article about a basketball playoff game. Based uponactions performed by the user while in the news domain (e.g., readingarticles about basketball), the user may decide to transition to adifferent domain, such as the shopping website having the shoppingdomain. While accessing the shopping website, the user may perform anaction such as purchasing a basketball jersey because the user recentlyread the basketball articles while in the news domain.

SUMMARY

In accordance with the present disclosure, one or more computing devicesand/or methods for cross-domain action prediction are provided. Aservice switch classifier is trained to predict whether users willswitch between domains (e.g., switching from reading movie reviewswithin a news domain to purchasing a movie from a movie streamingservice having a streaming domain), and to recommend actions to usersthat relate to the domains to which the users are predicted to switch(e.g., a recommendation to sign up for a movie club newsletter). Adomain may correspond to an application, a service, a website, aplatform, or other provider or content, which may correspond to acertain domain topics, such as a news domain, a shopping domain, asocial network domain, a sports domain, a job search domain, anentertainment domain, a gaming domain, and/or a variety of other domaintopics.

While users are performing actions in one domain (e.g., reading a moviereview from a movie review service), the users may switch to anotherdomain (e.g., purchasing a movie through a streaming service) due toinformation learned in the prior domain. This information may becollected as past user action sequences. The past user action sequencesmay be utilized as heterogeneous inputs from different domains andservices, which can be integrated into a comprehensive understanding ofuser behavior. The past user action sequences are series of actionsdefined as action chains, in which users switch between differentdomains/services given intent transitions due to information collectedin prior domains/services (e.g., while reading movie reviews, the userforms an interest in a particular movie to purchase).

The past user action sequences, corresponding to past user actionsperformed by users across a plurality of domains (e.g., a user reading amovie review, then visiting a social network group for discussingmovies, and then finally renting a movie from a movie streamingservice), are used to train a graph embedding. For example, a graph maybe constructed from the past user action sequences. A DeepWalk techniquemay be applied to the graph to extent the past user action sequences. Inthis way, relationships between different actions (e.g., reading a moviereview and then purchasing a movie) may be learned as relationalknowledge.

Action sequence embeddings are generated based upon the graph embedding(e.g., the relational knowledge of relationships between differentactions) and a textual embedding applied to the past user actionsequences. In an example of the textual embedding, textual content suchas descriptive information associated with past user actions (e.g., textof a content item interacted with by a user such as text of a moviereview, a categorical topic of the content item, publisher details,etc.) is taken into account to define textual content of actions forinclusion within the action sequence embeddings.

An autoencoder is trained using the action sequence embeddings toproject the action sequence embeddings to obtain intent space vectors.Each action sequence embedding is projected into its own intent spacevector to represent intent of the user (e.g., intent to transition to aparticular domain/service, intent to perform a particular action withina particular domain/service, etc.) while performing actions within aparticular action sequence embedding. A service switch classifier istrained using the intent space vectors to predict a domain that a userintends to use and actions that the use would likely perform in thatdomain.

For a current user of a current domain (e.g., a user currently readingabout the housing market on a financial advice website), the serviceswitch classifier is used to predict whether the current user willswitch from the current domain (e.g., a financial domain) to aparticular next domain. A current user action sequence for the user isdetermined. The current user action sequence may correspond to actionsthat the user has recently performed, such as reading various housingmarket articles, viewing mortgage interest rates, etc.). The actionsequence embeddings are applied to the current user action sequence togenerate a current action sequence embedding. The current actionsequence embedding is used to predict whether the current user willswitch from the current domain (e.g., the financial domain) to a nextdomain (e.g., a real estate domain). The current action sequenceembedding is projected into a current intent space vector for thecurrent user. The current intent space vector is used as input into aplurality of classifiers that will each output predictions as to whetherthe current user will switch domains from the current domain to the nextdomain. A voter mechanism is used to combine the outputs from theclassifiers to determine a final prediction corresponding to aprobability of the user switching from the current domain to the nextdomain. If the probability exceeds a threshold, then the service switchclassifier is used to identify one or more actions to recommend to thecurrent user. The one or more actions may correspond to the next domain(e.g., a link to a real estate website, a recommendation of a realtor, adescription of a house for sale, etc.). The one or more actions may bepresented to the current user, such as displayed through a userinterface used by the current user to access the current domain.

DESCRIPTION OF THE DRAWINGS

While the techniques presented herein may be embodied in alternativeforms, the particular embodiments illustrated in the drawings are only afew examples that are supplemental of the description provided herein.These embodiments are not to be interpreted in a limiting manner, suchas limiting the claims appended hereto.

FIG. 1 is an illustration of a scenario involving various examples ofnetworks that may connect servers and clients.

FIG. 2 is an illustration of a scenario involving an exampleconfiguration of a server that may utilize and/or implement at least aportion of the techniques presented herein.

FIG. 3 is an illustration of a scenario involving an exampleconfiguration of a client that may utilize and/or implement at least aportion of the techniques presented herein.

FIG. 4 is a flow chart illustrating an example method for cross-domainaction prediction.

FIG. 5 is an illustration of an example of a user domain transition.

FIG. 6 is an illustration of an example of a user domain transition.

FIG. 7 is a component block diagram illustrating an example system forcross-domain action prediction.

FIG. 8 is an illustration of an example of providing a recommendationbased upon cross-domain action prediction.

FIG. 9 is an illustration of a scenario featuring an examplenon-transitory machine readable medium in accordance with one or more ofthe provisions set forth herein.

DETAILED DESCRIPTION

Subject matter will now be described more fully hereinafter withreference to the accompanying drawings, which form a part hereof, andwhich show, by way of illustration, specific example embodiments. Thisdescription is not intended as an extensive or detailed discussion ofknown concepts. Details that are known generally to those of ordinaryskill in the relevant art may have been omitted, or may be handled insummary fashion.

The following subject matter may be embodied in a variety of differentforms, such as methods, devices, components, and/or systems.Accordingly, this subject matter is not intended to be construed aslimited to any example embodiments set forth herein. Rather, exampleembodiments are provided merely to be illustrative. Such embodimentsmay, for example, take the form of hardware, software, firmware or anycombination thereof.

1. Computing Scenario

The following provides a discussion of some types of computing scenariosin which the disclosed subject matter may be utilized and/orimplemented.

1.1. Networking

FIG. 1 is an interaction diagram of a scenario 100 illustrating aservice 102 provided by a set of servers 104 to a set of client devices110 via various types of networks. The servers 104 and/or client devices110 may be capable of transmitting, receiving, processing, and/orstoring many types of signals, such as in memory as physical memorystates.

The servers 104 of the service 102 may be internally connected via alocal area network 106 (LAN), such as a wired network where networkadapters on the respective servers 104 are interconnected via cables(e.g., coaxial and/or fiber optic cabling), and may be connected invarious topologies (e.g., buses, token rings, meshes, and/or trees). Theservers 104 may be interconnected directly, or through one or more othernetworking devices, such as routers, switches, and/or repeaters. Theservers 104 may utilize a variety of physical networking protocols(e.g., Ethernet and/or Fiber Channel) and/or logical networkingprotocols (e.g., variants of an Internet Protocol (IP), a TransmissionControl Protocol (TCP), and/or a User Datagram Protocol (UDP). The localarea network 106 may include, e.g., analog telephone lines, such as atwisted wire pair, a coaxial cable, full or fractional digital linesincluding T1, T2, T3, or T4 type lines, Integrated Services DigitalNetworks (ISDNs), Digital Subscriber Lines (DSLs), wireless linksincluding satellite links, or other communication links or channels,such as may be known to those skilled in the art. The local area network106 may be organized according to one or more network architectures,such as server/client, peer-to-peer, and/or mesh architectures, and/or avariety of roles, such as administrative servers, authenticationservers, security monitor servers, data stores for objects such as filesand databases, business logic servers, time synchronization servers,and/or front-end servers providing a user-facing interface for theservice 102.

Likewise, the local area network 106 may comprise one or moresub-networks, such as may employ differing architectures, may becompliant or compatible with differing protocols and/or may interoperatewithin the local area network 106. Additionally, a variety of local areanetworks 106 may be interconnected; e.g., a router may provide a linkbetween otherwise separate and independent local area networks 106.

In the scenario 100 of FIG. 1, the local area network 106 of the service102 is connected to a wide area network 108 (WAN) that allows theservice 102 to exchange data with other services 102 and/or clientdevices 110. The wide area network 108 may encompass variouscombinations of devices with varying levels of distribution andexposure, such as a public wide-area network (e.g., the Internet) and/ora private network (e.g., a virtual private network (VPN) of adistributed enterprise).

In the scenario 100 of FIG. 1, the service 102 may be accessed via thewide area network 108 by a user 112 of one or more client devices 110,such as a portable media player (e.g., an electronic text reader, anaudio device, or a portable gaming, exercise, or navigation device); aportable communication device (e.g., a camera, a phone, a wearable or atext chatting device); a workstation; and/or a laptop form factorcomputer. The respective client devices 110 may communicate with theservice 102 via various connections to the wide area network 108. As afirst such example, one or more client devices 110 may comprise acellular communicator and may communicate with the service 102 byconnecting to the wide area network 108 via a wireless local areanetwork 106 provided by a cellular provider. As a second such example,one or more client devices 110 may communicate with the service 102 byconnecting to the wide area network 108 via a wireless local areanetwork 106 provided by a location such as the user's home or workplace(e.g., a WiFi (Institute of Electrical and Electronics Engineers (IEEE)Standard 802.11) network or a Bluetooth (IEEE Standard 802.15.1)personal area network). In this manner, the servers 104 and the clientdevices 110 may communicate over various types of networks. Other typesof networks that may be accessed by the servers 104 and/or clientdevices 110 include mass storage, such as network attached storage(NAS), a storage area network (SAN), or other forms of computer ormachine readable media.

1.2. Server Configuration

FIG. 2 presents a schematic architecture diagram 200 of a server 104that may utilize at least a portion of the techniques provided herein.Such a server 104 may vary widely in configuration or capabilities,alone or in conjunction with other servers, in order to provide aservice such as the service 102.

The server 104 may comprise one or more processors 210 that processinstructions. The one or more processors 210 may optionally include aplurality of cores; one or more coprocessors, such as a mathematicscoprocessor or an integrated graphical processing unit (GPU); and/or oneor more layers of local cache memory. The server 104 may comprise memory202 storing various forms of applications, such as an operating system204; one or more server applications 206, such as a hypertext transportprotocol (HTTP) server, a file transfer protocol (FTP) server, or asimple mail transport protocol (SMTP) server; and/or various forms ofdata, such as a database 208 or a file system. The server 104 maycomprise a variety of peripheral components, such as a wired and/orwireless network adapter 214 connectible to a local area network and/orwide area network; one or more storage components 216, such as a harddisk drive, a solid-state storage device (SSD), a flash memory device,and/or a magnetic and/or optical disk reader.

The server 104 may comprise a mainboard featuring one or morecommunication buses 212 that interconnect the processor 210, the memory202, and various peripherals, using a variety of bus technologies, suchas a variant of a serial or parallel AT Attachment (ATA) bus protocol; aUniform Serial Bus (USB) protocol; and/or Small Computer SystemInterface (SCI) bus protocol. In a multibus scenario, a communicationbus 212 may interconnect the server 104 with at least one other server.Other components that may optionally be included with the server 104(though not shown in the schematic architecture diagram 200 of FIG. 2)include a display; a display adapter, such as a graphical processingunit (GPU); input peripherals, such as a keyboard and/or mouse; and aflash memory device that may store a basic input/output system (BIOS)routine that facilitates booting the server 104 to a state of readiness.

The server 104 may operate in various physical enclosures, such as adesktop or tower, and/or may be integrated with a display as an“all-in-one” device. The server 104 may be mounted horizontally and/orin a cabinet or rack, and/or may simply comprise an interconnected setof components. The server 104 may comprise a dedicated and/or sharedpower supply 218 that supplies and/or regulates power for the othercomponents. The server 104 may provide power to and/or receive powerfrom another server and/or other devices. The server 104 may comprise ashared and/or dedicated climate control unit 220 that regulates climateproperties, such as temperature, humidity, and/or airflow. Many suchservers 104 may be configured and/or adapted to utilize at least aportion of the techniques presented herein.

1.3. Client Device Configuration

FIG. 3 presents a schematic architecture diagram 300 of a client device110 whereupon at least a portion of the techniques presented herein maybe implemented. Such a client device 110 may vary widely inconfiguration or capabilities, in order to provide a variety offunctionality to a user such as the user 112. The client device 110 maybe provided in a variety of form factors, such as a desktop or towerworkstation; an “all-in-one” device integrated with a display 308; alaptop, tablet, convertible tablet, or palmtop device; a wearable devicemountable in a headset, eyeglass, earpiece, and/or wristwatch, and/orintegrated with an article of clothing; and/or a component of a piece offurniture, such as a tabletop, and/or of another device, such as avehicle or residence. The client device 110 may serve the user in avariety of roles, such as a workstation, kiosk, media player, gamingdevice, and/or appliance.

The client device 110 may comprise one or more processors 310 thatprocess instructions. The one or more processors 310 may optionallyinclude a plurality of cores; one or more coprocessors, such as amathematics coprocessor or an integrated graphical processing unit(GPU); and/or one or more layers of local cache memory. The clientdevice 110 may comprise memory 301 storing various forms ofapplications, such as an operating system 303; one or more userapplications 302, such as document applications, media applications,file and/or data access applications, communication applications such asweb browsers and/or email clients, utilities, and/or games; and/ordrivers for various peripherals. The client device 110 may comprise avariety of peripheral components, such as a wired and/or wirelessnetwork adapter 306 connectible to a local area network and/or wide areanetwork; one or more output components, such as a display 308 coupledwith a display adapter (optionally including a graphical processing unit(GPU)), a sound adapter coupled with a speaker, and/or a printer; inputdevices for receiving input from the user, such as a keyboard 311, amouse, a microphone, a camera, and/or a touch-sensitive component of thedisplay 308; and/or environmental sensors, such as a global positioningsystem (GPS) receiver 319 that detects the location, velocity, and/oracceleration of the client device 110, a compass, accelerometer, and/orgyroscope that detects a physical orientation of the client device 110.Other components that may optionally be included with the client device110 (though not shown in the schematic architecture diagram 300 of FIG.3) include one or more storage components, such as a hard disk drive, asolid-state storage device (SSD), a flash memory device, and/or amagnetic and/or optical disk reader; and/or a flash memory device thatmay store a basic input/output system (BIOS) routine that facilitatesbooting the client device 110 to a state of readiness; and a climatecontrol unit that regulates climate properties, such as temperature,humidity, and airflow.

The client device 110 may comprise a mainboard featuring one or morecommunication buses 312 that interconnect the processor 310, the memory301, and various peripherals, using a variety of bus technologies, suchas a variant of a serial or parallel AT Attachment (ATA) bus protocol;the Uniform Serial Bus (USB) protocol; and/or the Small Computer SystemInterface (SCI) bus protocol. The client device 110 may comprise adedicated and/or shared power supply 318 that supplies and/or regulatespower for other components, and/or a battery 304 that stores power foruse while the client device 110 is not connected to a power source viathe power supply 318. The client device 110 may provide power to and/orreceive power from other client devices.

In some scenarios, as a user 112 interacts with a software applicationon a client device 110 (e.g., an instant messenger and/or electronicmail application), descriptive content in the form of signals or storedphysical states within memory (e.g., an email address, instant messengeridentifier, phone number, postal address, message content, date, and/ortime) may be identified. Descriptive content may be stored, typicallyalong with contextual content. For example, the source of a phone number(e.g., a communication received from another user via an instantmessenger application) may be stored as contextual content associatedwith the phone number. Contextual content, therefore, may identifycircumstances surrounding receipt of a phone number (e.g., the date ortime that the phone number was received), and may be associated withdescriptive content. Contextual content, may, for example, be used tosubsequently search for associated descriptive content. For example, asearch for phone numbers received from specific individuals, receivedvia an instant messenger application or at a given date or time, may beinitiated. The client device 110 may include one or more servers thatmay locally serve the client device 110 and/or other client devices ofthe user 112 and/or other individuals. For example, a locally installedwebserver may provide web content in response to locally submitted webrequests. Many such client devices 110 may be configured and/or adaptedto utilize at least a portion of the techniques presented herein.

2. Presented Techniques

One or more systems and/or techniques for cross-domain action predictionare provided. Users may perform tasks by performing actions acrossmultiple domains, such across various services, applications, platforms,and/or websites having different domain types. A domain may correspondto a particular type of service, application, platform, website, etc.,such as a news domain, a sports domain, an entertainment domain, afinance domain, a social network domain, an application store domain, astreaming service domain, a real estate domain, etc. Users may typicallysearch for information using different types (domains) of websites asopposed to a single type (domain) of website. For example, a user mayread book reviews through a social network book club page having asocial network domain, then read about upcoming book releases through anentertainment section of a news website having a news domain, and mayfinally purchase a book through a shopping application having a shoppingdomain. As the user performs actions across the various domains (e.g.,reading content items such as articles, clicking links, viewing images,signing up for newsletters, watching a video, submitting search queries,etc.), the user may learn more information that leads the user totransition to other domains.

Merely taking into account actions by a user within a single domain(e.g., only actions taken by the user while interacting with the socialnetwork book club page as opposed to also the news website) whendetermining user behavior and intent will lead to imprecise predictionsof what a current user may intend to do next. This will lead torecommendations of actions and content items that may be irrelevant tothe current user because a wrong or imprecise user intent was predicted.This wastes computing resources, network bandwidth, and may frustrate orannoy the user with being provided with irrelevant content andrecommendations.

Accordingly, as provided herein, user actions across multiple domainsmay be taken into account when predicting user intent. A service switchclassifier is trained using action sequence embeddings derived fromgraph embeddings and textual embeddings that are applied to past useraction sequences corresponding to sequences of past actions performed byusers across a plurality of domains. The switch classifier may also betrained using additional information, such as timespan between twodifferent consecutive actions being performed, a time at which an actionis executed, a length of content upon which an action is executed (e.g.,a number of characters within an article read by a user), a type ofcurrent action (e.g., reading an article, viewing an image, watching avideo, creating a social network post, clicking a link, submittinginformation, searching content of a video streaming service, purchasingan item or service, reading an email, etc.).

The service switch classifier is trained to predict a probability that auser will switch from a current domain to a particular next domain(e.g., the user switching from reading articles regarding outdoor patioideas in a gardening domain to looking for landscapers through acontractor website within a contractor service domain). If theprobability exceeds a threshold that the user will switch from thecurrent domain to the next domain, then the service switch classifier isused to identify actions to recommend to the user. The actions may berelated to the next domain (e.g., a recommendation of a landscaper maybe provided to the user while the user is still within the gardeningdomain). In this way, more precise and relevant recommendations may begenerating and displayed to users.

An embodiment of cross-domain action prediction is illustrated by anexample method 400 of FIG. 4. A service switch classifier (e.g., aservice switch classifier 701 of FIG. 7) may be trained using past useraction sequences corresponding to sequences of past actions performed byusers across a plurality of domains. The past user action sequences maybe tracked as users perform actions through various websites, platforms,services, applications, etc. In an example of tracking past user actionsequences, FIG. 5 illustrates an example 500 of a past user actionsequence that comprises actions performed across multiple domains. Forexample, a user may utilize a computing device 502 (e.g., a mobiledevice, a laptop, a computer, a tablet, etc.) to access a news website504 having a news domain classification. The user may perform variousactions while accessing the news website 504, such as clicking links,watching videos, submitting information, reading articles, etc. Forexample, the user may read a news story 506 regarding a new videogameconsole that is going to release on Friday. These actions may be trackedas past user actions that may be grouped into past user action sequences(e.g., if two consecutive actions occur within a threshold timespan ofone another such as within 30 minutes or any other timespan, then thetwo consecutive actions may be considered part of a same past useraction sequence).

After reading the news story 506 regarding the new videogame consolerelease, the user may form a user intent to preorder the new videogameconsole. Accordingly, the user may perform a user domain transition 508to transition from the news website 504 having the news domainclassification to a shopping website 510 having a shopping domainclassification (e.g., a videogame shopping domain classification). Theuser may perform various actions while accessing the shopping website510, such a viewing videogame reviews, submitting search queries, addingitems into a shopping cart, making purchases, etc. For example, the usermay utilize a videogame consoles purchase user interface 512 of theshopping website 510 to preorder the new videogame console. Theseactions may be tracked, along with the actions performed by the userwhile at the news website 504 of the news domain, as past user actionsthat may be grouped into past user action sequences (e.g., if twoconsecutive actions occur within a threshold timespan of one anothersuch as within 30 minutes or any other timespan, then the twoconsecutive actions may be considered part of a same past user actionsequence). For example, a user action sequence may include the userreading the news story 506 while at the news website 504 having the newsdomain and preordering the new videogame console while at the shoppingwebsite 510 having the shopping domain. In this way, a user actionsequence may include actions performed across multiple domains asopposed to a single domain.

In another example of tracking past user action sequences, FIG. 6illustrates an example 600 of a past user action sequence that comprisesactions performed across multiple domains. For example, a user mayutilize a computing device 602 (e.g., a mobile device, a laptop, acomputer, a tablet, etc.) to access a video streaming application 604having a video domain classification. The user may perform variousactions while accessing the video streaming application 604, such asrenting movies, submitting search queries, reading movie reviews,submitting user reviews, rating movies, etc. For example, the user maywatch a soccer training video 606. These actions may be tracked as pastuser actions that may be grouped into past user action sequences (e.g.,if two consecutive actions occur within a threshold timespan of oneanother such as within 20 minutes or any other timespan, then the twoconsecutive actions may be considered part of a same past user actionsequence).

After watching the soccer training video 606, the user may form a userintent to buy a soccer ball. Accordingly, the user may perform a userdomain transition 608 to transition from the video streaming application604 having the video domain classification to a sports website 610having a shopping domain classification (e.g., a sports shopping domainclassification). The user may perform various actions while accessingthe sports website 610, such a chatting about sports, viewing soccernews, viewing sports scores, shopping for sports apparel, etc. Forexample, the user may utilize a soccer apparel shopping user interface612 of the sports website 610 to purchase the soccer ball. These actionsmay be tracked, along with the actions performed by the user while usingthe video streaming application 604 of the video domain, as past useractions that may be grouped into past user action sequences (e.g., iftwo consecutive actions occur within a threshold timespan of one anothersuch as within 20 minutes or any other timespan, then the twoconsecutive actions may be considered part of a same past user actionsequence). For example, a user action sequence may include the userwatching the soccer training video 606 while using the video streamingapplication 604 having the video domain and purchasing the soccer ballwhile at the sports website 610 having the shopping domain. In this way,a user action sequence may include actions performed across multipledomains as opposed to a single domain.

At 402 of method 400, the past user action sequences are used to train agraph embedding to obtain relational knowledge corresponding torelationships between actions performed by users (e.g., a relationshipbetween a user viewing a sports video and purchasing a soccer ball, arelationship between a user reading movie reviews and posting a socialnetwork post about movie suggestions, etc.). The graph embedding may beused to extend the past user action sequences with the relationalknowledge and relationships between actions. In an embodiment, a pastuser action sequence may be represented as a path within a network, suchas a directed graph. Each action is a node within the directed graph,and edges between nodes represent action-to-action links. The directedgraph is processed, such as walked using a DeepWalk algorithm, to learnlow-dimensional representations of each node (each action) by simulatingmultiple random walks within the directed graph in order to generaterelational representations for each node (each action) within thedirected graph. Node sequences (user action sequences) may be generatedbased upon the random walks. A representation for a node may comprise amatrix mapping trainable parameters and a hidden dimension. A skip-gramalgorithm may also be applied. In this way, the graph embedding istrained/generated based upon the past user action sequences.

At 404, action sequence embeddings are generated based upon the graphembedding and based upon a textual embedding applied to the past useraction sequences. In an example, a bidirectional encoder representationtransformer (BERT) or other natural language processing functionality(e.g., an encoder-decoder model that utilizing masking techniques) maybe used for the textual embedding. For example, an encoder structure maybe built by defining textual content of actions (e.g., text of anarticle read by a user), which is transformed using word segmentation tocreate word representations. A key-value memory network is applied tothe word representations, which are split into vectors (e.g., 3 vectors)corresponding to linear mappings. A decoder may be defined in a similarmanner. The encoder and decoder (e.g., a 7 layer fully connectedautoencoder) may be used to embed textual knowledge for the past useraction sequences. In this way, action sequence embeddings are generatedbased upon the graph embedding and the textual embedding.

At 406, an autoencoder may be trained utilizing the action sequenceembeddings to project the action sequence embeddings to obtain intentspace vectors. Each action sequence embedding is projected to obtain acorresponding intent space vector (e.g., a first intent space vectorcorresponds to a projection of a first action sequence embedding; asecond intent space vector corresponds to a projection of a secondaction sequence embedding; etc.). In this way, the autoencoder (e.g., along short-term memory (LSTM) autoencoder) transfers the past useractions within the action sequence embeddings into intent space vectorscorresponding to user intents (e.g., actions corresponding to an intentof a user to switch to a particular domain and/or to perform aparticular action).

The autoencoder may be trained based upon various additionalinformation. In an example, the autoencoder may be trained based upontime spent metrics. A time spent metric corresponds to a time differencebetween two consecutive actions within the past user action sequences(e.g., a time between a user reading an article and the usersubsequently submitting a search query). In an example, the autoencodermay be trained based upon times at which actions within the past useraction sequences were performed. In an example, the autoencoder may betrained based upon content sizes of content items (e.g., a number ofcharacters, words, etc. within an article read by a user) upon whichactions within the past user action sequences were performed. In anexample, the autoencoder may be trained based upon content types ofcontent items upon which actions within the past user action sequenceswere performed (e.g., text, an article, a search query, a video, animage, a social network post, etc.).

At 408, the service switch classifier is trained using the intent spacevectors. The service switch classifier is trained to identify patternsin user behavior (user actions) that correspond to switching from onedomain to another domain. For example, the intent space vectors mayindicate that a user has an intent to switch to a videogame shoppingdomain and/or to perform a videogame console preorder action within thevideogame shopping domain after reading videogame console releasearticles within a news domain. The service switch classifier can betrained to identify this intent to switch to the videogame shoppingdomain and/or to perform the videogame console preorder action bylearning such patterns from the intent space vectors.

At 410, the service switch classifier is utilized to predict whetherusers will switch between domains and/or to recommend actions (e.g.,viewing a content item, signing up for a newsletter, viewing an image,watching a video, buying a product, downloading an application,listening to a song, subscribing to a service, etc.) associated with thedomains to which the user are predicted to switch. In an example, acurrent user may be interacting with one or more services, platforms,websites, applications, etc. For example, the current user may becurrently reading an article about coffee makers through a news mobileapplication executing on a user device. A current action sequence of thecurrent user reading the article may be identified. The current actionsequence of the current user may also comprise other actions while theuser is interacting with the news mobile application executing on theuser device. The current action sequence of the current user may alsocomprise other actions that the user performed while using otherapplications, websites, service, and/or platforms that may have the sameor different domain as the news mobile application. In this way, useractions (e.g., consecutive user actions that are performed within athreshold timespan of one another such as 20 minutes or any othertimespan) may be identified and tracked as the current action sequenceof the current user by the service switch classifier.

The service switch classifier may apply the action sequence embeddingsto the current user action sequence of the current user to generate acurrent action sequence embedding. The current action sequence embeddingmay be used by the service switch classifier to predict whether thecurrent user will switch from a current domain (e.g., the news mobileapplication executing on the user device) to a next domain (e.g., a homeappliance shopping website). In particular, the current action sequenceembedding may be projected by the service switch classifier to obtain acurrent intent space vector for the current user. The current intentspace vector may be evaluated by the service switch classifier togenerate a prediction as to whether the current user will switch fromthe current domain to the next domain. For example, the current intentspace vector of the current user may be utilized as input into aplurality of classifiers by the service switch classifier for generatingthe prediction of whether the current user will switch from the currentdomain to the next domain. The service switch classifier may utilize avoter mechanism to combine the outputs from the plurality of classifiersto determine the prediction of whether the current user will switch fromthe current domain to the next domain. The voter mechanism may utilize adecision tree as a structure for evaluating and combining the outputsfrom the plurality of classifiers. In an embodiment, the prediction maycorrespond to a probability (a likelihood) that the current user willswitch from the current domain to the next domain. If the prediction hasa probability above a threshold (e.g., more than 50% of the plurality ofclassifiers predict that the current user will switch from the currentdomain to the next domain), then the current user is predicted to switchfrom the current domain to the next domain.

If the current user is predicted to switch from the current domain tothe next domain (e.g., the prediction corresponds to a probability thatthe current user has an intent to switch to the next domain above athreshold), then the service switch classifier utilizes the currentintent space vector and the intent space vectors, derived from theaction sequence embeddings of the past user action sequences, todetermine a next action to recommend to the current user. The nextaction may correspond to an action that can be performed within the nextdomain. For example, the next action may correspond to a recommendationto read coffee marker reviews of coffee makers available for purchasethrough the home appliance shopping website. In determining the nextaction, the service switch classifier may identify a threshold number ofnearest intent space vectors (K nearest intent space vectors) inrelation to the current intent space vector. The threshold number ofnearest intent space vectors are determined because users with similarintents will behave similarly (e.g., users with an intent to learn moreabout coffee makers may read coffee maker reviews and/or purchase coffeemakers). A current intent of the current user (e.g., an intent to learnmore about coffee markers) may be determined based upon user actionsequences of the threshold number of nearest intent space vectors (e.g.,past actions performed by other users that had similar intent to learnabout coffee makers). Actions, of the user action sequences of thethreshold number of nearest intent space vectors, may be recommended tothe user. For example, a link to coffee maker reviews within the homeappliance shopping website may be provided to the user (e.g., the linkmay be emailed to the user, sent as a text message to the user,displayed to the user through the news mobile application, etc.).

FIG. 7 illustrates an example system 700 comprising a service switchclassifier 701 configured for cross-domain action prediction. Theservice switch classifier 701 may be implemented as software, hardware,or a combination thereof (e.g., a service executing on one or moreservers). The service switch classifier 701 may be invoked to performcross-domain action prediction in response to a current user accessing adomain (e.g., the current user executing an application on a mobiledevice, accessing a website, accessing a service, etc.). During a datapre-processing phase 702, the service switch classifier 701 may trackand identify current user behavior 710 of the current user. The currentuser behavior 710 may correspond to actions performed by the currentuser (e.g., recent actions performed within a threshold amount of timefrom a current time, consecutive actions performed within a thresholdamount of time from one another, etc.). The service switch classifier701 may apply 716 one-hot encoding (e.g., natural language processing todistinguish between words of content items upon which the actions wereperformed by the current user) and/or normalization to the current userbehavior 710, which may be output as a current action sequence embeddingfor the current user (e.g., a normalized one-hot encoded vectorgenerated after one-hot encoding and/or normalization).

The service switch classifier 701 may access past user behaviors 708 ofusers performing actions across various domains (e.g., users submittingsearch queries, viewing movies, submitting social network posts, readingblogs, purchasing services or items, etc.). The service switchclassifier 701 may generate past user action sequences 712 based uponthe past user behaviors 708. A past user action sequence may compriseconsecutive actions performed by a user within a threshold amount oftime from one another. A graph embedding and/or a textual embedding maybe applied by the service switch classifier 701 to the past user actionsequences 712 to generate action sequence embeddings 714.

The current user behavior 710 (e.g., the current action sequenceembedding) and the action sequence embeddings 714 are input into anautoencoder 718 by the service switch classifier 701 during an actionsequence embedding phase 704. The autoencoder 718 may utilize longshort-term memory (LSTM) cells to learn representations of given actionsequences and conglomerate action sequences having similar patterns.During each training iteration, the action sequence embeddings 714and/or the current user behavior 710 (e.g., the current action sequenceembedding) are input into the long short-term memory cells that willrecursively update a hidden state. Hidden states of the autoencoder 718are concatenated together as hidden representations 720 of each actionsequence, which are projected into vector space as intent space vectorsderived from the action sequence embeddings 714 and a current intentspace vector derived from the current user behavior 710 of the currentuser (e.g., the current action sequence embedding). Because users withsimilar intentions are assumed to demonstrate similar patterns, intentspace vectors from the hidden representations 720 can be utilized tomake recommendations by conducting nearest-neighbor lookups 728 in thevector space during a prediction phase 706.

In an example, the intent space vectors may be utilized during theprediction phase 706 to perform a domain prediction 722 to identify anext domain 724 to which the user is predicted to switch. In anotherexample, the intent space vectors may be processed using thenearest-neighbor lookups 728 during the prediction phase 706 usingaction suggestion functionality 726 of the service switch classifier toidentify suggestions 730 of actions and/or content items to recommend tothe current user. The suggestions 730 may correspond to actions and/orcontent items associated with the next domain 724.

FIG. 8 illustrates an example 800 of cross-domain prediction. A currentuser may utilize a computing device 802 to access a news website 804associated with a news domain. A service switch classifier may trackactions by the current user as a current user action sequence. Forexample, the current user may read an article 806 relating to a newvideogame console that is to be released, along with performing otheractions such as watching a video describing the new videogame consoleand reading social network posts about the new videogame console. Theservice switch classifier may utilize the current user action sequenceand past user action sequences to predict that the current user willswitch to a videogame shopping domain. The service switch classifier mayutilize the current user action sequence and past user action sequencesto generate a recommendation 808 to perform a preorder action throughthe videogame shopping domain to preorder the new videogame console. Therecommendation 808 may be displayed on the computing device 802.

FIG. 9 is an illustration of a scenario 900 involving an examplenon-transitory machine readable medium 902. The non-transitory machinereadable medium 902 may comprise processor-executable instructions 912that when executed by a processor 916 cause performance (e.g., by theprocessor 916) of at least some of the provisions herein. Thenon-transitory machine readable medium 902 may comprise a memorysemiconductor (e.g., a semiconductor utilizing static random accessmemory (SRAM), dynamic random access memory (DRAM), and/or synchronousdynamic random access memory (SDRAM) technologies), a platter of a harddisk drive, a flash memory device, or a magnetic or optical disc (suchas a compact disk (CD), a digital versatile disk (DVD), or floppy disk).The example non-transitory machine readable medium 902 storescomputer-readable data 904 that, when subjected to reading 906 by areader 910 of a device 908 (e.g., a read head of a hard disk drive, or aread operation invoked on a solid-state storage device), express theprocessor-executable instructions 912. In some embodiments, theprocessor-executable instructions 912, when executed cause performanceof operations, such as at least some of the example method 400 of FIG.4, for example. In some embodiments, the processor-executableinstructions 912 are configured to cause implementation of a system,such as at least some of the example system 700 of FIG. 7, for example.

3. Usage of Terms

As used in this application, “component,” “module,” “system”,“interface”, and/or the like are generally intended to refer to acomputer-related entity, either hardware, a combination of hardware andsoftware, software, or software in execution. For example, a componentmay be, but is not limited to being, a process running on a processor, aprocessor, an object, an executable, a thread of execution, a program,and/or a computer. By way of illustration, both an application runningon a controller and the controller can be a component. One or morecomponents may reside within a process and/or thread of execution and acomponent may be localized on one computer and/or distributed betweentwo or more computers.

Unless specified otherwise, “first,” “second,” and/or the like are notintended to imply a temporal aspect, a spatial aspect, an ordering, etc.Rather, such terms are merely used as identifiers, names, etc. forfeatures, elements, items, etc. For example, a first object and a secondobject generally correspond to object A and object B or two different ortwo identical objects or the same object.

Moreover, “example” is used herein to mean serving as an example,instance, illustration, etc., and not necessarily as advantageous. Asused herein, “or” is intended to mean an inclusive “or” rather than anexclusive “or”. In addition, “a” and “an” as used in this applicationare generally be construed to mean “one or more” unless specifiedotherwise or clear from context to be directed to a singular form. Also,at least one of A and B and/or the like generally means A or B or both Aand B. Furthermore, to the extent that “includes”, “having”, “has”,“with”, and/or variants thereof are used in either the detaileddescription or the claims, such terms are intended to be inclusive in amanner similar to the term “comprising”.

Although the subject matter has been described in language specific tostructural features and/or methodological acts, it is to be understoodthat the subject matter defined in the appended claims is notnecessarily limited to the specific features or acts described above.Rather, the specific features and acts described above are disclosed asexample forms of implementing at least some of the claims.

Furthermore, the claimed subject matter may be implemented as a method,apparatus, or article of manufacture using standard programming and/orengineering techniques to produce software, firmware, hardware, or anycombination thereof to control a computer to implement the disclosedsubject matter. The term “article of manufacture” as used herein isintended to encompass a computer program accessible from anycomputer-readable device, carrier, or media. Of course, manymodifications may be made to this configuration without departing fromthe scope or spirit of the claimed subject matter.

Various operations of embodiments are provided herein. In an embodiment,one or more of the operations described may constitute computer readableinstructions stored on one or more computer readable media, which ifexecuted by a computing device, will cause the computing device toperform the operations described. The order in which some or all of theoperations are described should not be construed as to imply that theseoperations are necessarily order dependent. Alternative ordering will beappreciated by one skilled in the art having the benefit of thisdescription. Further, it will be understood that not all operations arenecessarily present in each embodiment provided herein. Also, it will beunderstood that not all operations are necessary in some embodiments.

Also, although the disclosure has been shown and described with respectto one or more implementations, equivalent alterations and modificationswill occur to others skilled in the art based upon a reading andunderstanding of this specification and the annexed drawings. Thedisclosure includes all such modifications and alterations and islimited only by the scope of the following claims. In particular regardto the various functions performed by the above described components(e.g., elements, resources, etc.), the terms used to describe suchcomponents are intended to correspond, unless otherwise indicated, toany component which performs the specified function of the describedcomponent (e.g., that is functionally equivalent), even though notstructurally equivalent to the disclosed structure. In addition, while aparticular feature of the disclosure may have been disclosed withrespect to only one of several implementations, such feature may becombined with one or more other features of the other implementations asmay be desired and advantageous for any given or particular application.

What is claimed is:
 1. A method, comprising: executing, on a processorof a computing device, instructions that cause the computing device toperform operations, the operations comprising: training a graphembedding utilizing past user action sequences corresponding tosequences of past actions performed by users across a plurality ofdomains; generating action sequence embeddings based upon a textualembedding applied to the past user action sequences and based upon thegraph embedding; training an autoencoder utilizing the action sequenceembeddings to project the action sequence embeddings to obtain intentspace vectors, wherein a first intent space vector corresponds to aprojection of a first action sequence embedding; training a serviceswitch classifier using the intent space vectors; and utilizing theservice switch classifier to predict whether users will switch betweendomains.
 2. The method of claim 1, wherein the utilizing comprises:applying the action sequence embeddings to a current user actionsequence of a current user to generate a current action sequenceembedding for predicting whether the current user will switch from acurrent domain to a next domain.
 3. The method of claim 2, comprising:projecting the current action sequence embedding to obtain a currentintent space vector.
 4. The method of claim 3, comprising: generating aprediction of whether the current user will switch from the currentdomain to the next domain based upon the current intent space vector. 5.The method of claim 3, comprising: utilizing the current intent spacevector as input into a plurality of classifiers for determining aprediction of whether the current user will switch from the currentdomain to the next domain.
 6. The method of claim 5, comprising:utilizing a voter mechanism to combine outputs from the plurality ofclassifiers to determine the prediction of whether the current user willswitch from the current domain to the next domain.
 7. The method ofclaim 6, wherein a decision tree is utilized as a structure forperforming the voter mechanism.
 8. The method of claim 4, comprising: inresponse to the prediction corresponding to a probability of the currentuser switching to the next domain above a threshold, utilizing thecurrent intent space vector and the intent space vectors to determine anext action to recommend to the current user.
 9. The method of claim 8,wherein the next action corresponds to an action that can be performedwithin the next domain.
 10. The method of claim 8, comprising:identifying a threshold number of nearest intent space vectors inrelation to the current intent space vector.
 11. The method of claim 10,comprising: determining that a current intent of the current usercorresponds to user action sequences of the threshold number of nearestintent space vectors.
 12. The method of claim 11, comprising:recommending actions, of the user action sequences of the thresholdnumber of nearest intent space vectors, to the current user.
 13. Acomputing device comprising: a processor; and memory comprisingprocessor-executable instructions that when executed by the processorcause performance of operations, the operations comprising: generatingaction sequence embeddings based upon a textual embedding and a graphembedding utilizing past user action sequences corresponding tosequences of past actions performed by users across a plurality ofdomains; training an autoencoder utilizing the action sequenceembeddings to project the action sequence embeddings to obtain intentspace vectors, wherein a first intent space vector corresponds to aprojection of a first action sequence embedding; training a serviceswitch classifier using the intent space vectors; and utilizing theservice switch classifier to predict whether users will switch betweendomains.
 14. The computing device of claim 13, comprising: applying theaction sequence embeddings to a current user action sequence of acurrent user to generate a current action sequence embedding; andutilizing the current action sequence embedding to determine aprediction as to whether the current user will switch from a currentdomain to a next domain.
 15. The computing device of claim 14,comprising: in response to the prediction corresponding to a probabilityof the user switching to the next domain above a threshold, utilizing acurrent intent space vector, derived from the current action sequenceembedding, and the intent space vectors to determine a next action torecommend to the current user.
 16. The computing device of claim 12,comprising: training the autoencoder based upon time spent metrics,wherein a time spent metric corresponds to a time difference between twoconsecutive actions within the past user action sequences.
 17. Thecomputing device of claim 12, comprising: training the autoencoder basedupon times at which actions within the past user action sequences wereperformed.
 18. The computing device of claim 12, comprising: trainingthe autoencoder based upon content sizes of content items upon whichactions within the past user action sequences were performed.
 19. Thecomputing device of claim 12, comprising: training the autoencoder basedupon content types of content items upon which actions within the pastuser action sequences were performed.
 20. A non-transitory machinereadable medium having stored thereon processor-executable instructionsthat when executed cause performance of operations, the operationscomprising: generating action sequence embeddings based upon a textualembedding and a graph embedding utilizing past user action sequencescorresponding to sequences of past actions performed by users across aplurality of domains; training an autoencoder utilizing the actionsequence embeddings to project the action sequence embeddings to obtainintent space vectors, wherein a first intent space vector corresponds toa projection of a first action sequence embedding; training a serviceswitch classifier using the intent space vectors; and in response to theservice switch classifier predicting that a current user will switchfrom a current domain to a next domain, providing the current user witha recommendation of an action corresponding to the next domain.